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Record W2807716835 · doi:10.1109/icaset.2018.8376831

Review — Challenges of mobility aware MAC protocols in WSN

2018· article· en· W2807716835 on OpenAlexaff
Sijo Sudheendran, Ouns Bouachir, Sherif Moussa, Adel Omar Dahmane

Bibliographic record

Venue2018 Advances in Science and Engineering Technology International Conferences (ASET) · 2018
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsComputer networkComputer scienceWireless sensor networkNetwork packetEnergy consumptionThroughputProtocol (science)WirelessTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In today's smart world WSN plays an important role in IoT. The WSN nodes can be used for wildlife, patient, air quality monitoring. WSN consists of numerous sensor nodes that are connected to each other. One of the major concerns of WSN is the mobility of nodes. The mobility of the nodes creates concern to the MAC protocols that's defined for WSN static nodes. Mobile-WSN demands the participated nodes to send packets with a bursty traffic, low energy consumption and reliable connection. MAC protocol is the most important in designing the WSN as MAC plays an important role regarding throughput, mobility, security and energy consumption. This paper gives a review on mobility aware protocols such as M-MAC, MA-MAC, MMH-MAC, M-Contiki, MobiIQ, MobiDisc.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.311
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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